{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import simulation\n",
    "import random\n",
    "import pickle\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def simulate_random(n, bubble_sizes, n_samples=128, out_channels=2, alpha_major=1.11e-4, alpha_minor=1.9e-5, dt=0.01):\n",
    "    if bubble_sizes is None:\n",
    "        bubble_sizes = [1]\n",
    "\n",
    "    data = []\n",
    "\n",
    "    for i in tqdm(range(n_samples)):\n",
    "        size = random.choice(bubble_sizes)\n",
    "\n",
    "        xyz_bubble = np.random.randint(1, n-size, size=3)\n",
    "        bubble = np.stack([xyz_bubble, xyz_bubble+size], axis=1)\n",
    "\n",
    "        label = np.concatenate([bubble.mean(axis=1)/n, [size/n]])\n",
    "\n",
    "        alpha = np.ones((n, n, n)) * alpha_major\n",
    "        alpha[bubble[0,0]:bubble[0,1],bubble[1,0]:bubble[1,1],bubble[2,0]:bubble[2,1]] = alpha_minor\n",
    "        T = simulation.simulate_heat_conduction(alpha, out_channels=out_channels, t_diff=5, dt=dt)\n",
    "\n",
    "        data.append((T, label)) \n",
    "        \n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████| 1024/1024 [06:57<00:00,  2.45it/s]\n"
     ]
    }
   ],
   "source": [
    "alpha_Cu = 1.11e-4\n",
    "alpha_air = 1.9e-5\n",
    "\n",
    "data=simulate_random(n=32, bubble_sizes=[1,2,3,4,5], n_samples=1024, out_channels=2, alpha_major=alpha_Cu, alpha_minor=alpha_air, dt=0.01)\n",
    "#data=simulate_random(n=64, bubble_sizes=[2,3,4,5,6,7,8,9,10], n_samples=64, out_channels=2, alpha_major=alpha_Cu, alpha_minor=alpha_air, dt=0.0025)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = f'./data/heat_cond_data_n32_2.pickle'\n",
    "with open(filename, mode='wb') as output_file:\n",
    "    pickle.dump(data, output_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.625   0.65625 0.1875  0.125  ]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t, l = data[0]\n",
    "print(l)\n",
    "simulation.plot_top_temperature(t, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
